Real-Time Contingency Analysis on Massively Parallel Architectures With Compensation Method
Bibliographic record
Abstract
Real-time contingency analysis (RTCA) is paramount for modern power systems as it forms the basis for important operator actions that help to improve system stability, optimize generator dispatch, manage disparate resources, prevent cascading outages, and enhance market operations. With increasing system size and the number of contingency scenarios, RTCA is faced with computational challenges. To alleviate this situation, massively parallel graphics processing units (GPUs) are introduced for the acceleration of RTCA solution in this paper, where the compensation method (CM) is utilized for the concurrent AC power flow solution. Strategies and principles on the data structure, kernel function, and memory management are provided. Five benchmark systems (ranging from 300to 13,659-bus) are employed for case studies. Based on the sequential CM implemented on single-thread CPU, the performance analysis related to execution time and speedup is carried out for parallel CMs running on other architectures, including multi-thread CPU, single-GPU, and multi-GPUs. Results indicate that the parallel CM with multi-GPUs has sufficient accuracy, convergence, and scalability. Finally, the potential of the proposal for practical RTCA has been discussed with the reviewing of other state-of-the-art parallel computing methods reported in the literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".